sampling_vertex_dp

sampling_vertex_dp(n, polygon=None)

Simplify a polyline using the Douglas-Peucker algorithm.

Parameters:

nint

Number of items to return.

polygonbool, default=None

If True, the input data is considered as a polygon rings. If False, the input data is considered as a polyline. None for auto-detection.

Returns:

FeatureSpec

Vertices sample specification.

Notes

Vertex sampling is designed for polygon simplification.

Examples

import numpy as np
from scipy.stats import multivariate_normal
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(42)
n = 300
x = np.linspace(-1, 1, n)
y = np.linspace(-1, 1, n)
X, Y = np.meshgrid(x, y)
mean = np.zeros(2)
cov = [[1, .5],
       [.5, 1]]
rv = multivariate_normal(mean, cov)
Z = rv.pdf(np.dstack((X, Y)))
data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
ggplot(data, aes(x='x', y='y', z='z')) + \
    geom_contour(sampling=sampling_vertex_dp(100))